Added new demo

llm_app_personalized_memory/README.md

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+## 🧠 LLM App with Memory
+This Streamlit app is an AI-powered chatbot that uses OpenAI's GPT-4o model with a persistent memory feature. It allows users to have conversations with the AI while maintaining context across multiple interactions.
+
+### Features
+
+- Utilizes OpenAI's GPT-4o model for generating responses
+- Implements persistent memory using Mem0 and Qdrant vector store
+- Allows users to view their conversation history
+- Provides a user-friendly interface with Streamlit
+
+
+### How to get Started?
+
+1. Clone the GitHub repository
+```bash
+git clone https://github.com/Shubhamsaboo/awesome-llm-apps.git
+```
+
+2. Install the required dependencies:
+
+```bash
+pip install -r requirements.txt
+```
+
+3. Ensure Qdrant is running:
+The app expects Qdrant to be running on localhost:6333. Adjust the configuration in the code if your setup is different.
+
+```bash
+docker pull qdrant/qdrant
+
+docker run -p 6333:6333 -p 6334:6334 \
+    -v $(pwd)/qdrant_storage:/qdrant/storage:z \
+    qdrant/qdrant
+```
+
+4. Run the Streamlit App
+```bash
+streamlit run llm_app_memory.py
+```

llm_app_personalized_memory/llm_app_memory.py

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+import streamlit as st
+from mem0 import Memory
+from openai import OpenAI
+
+st.title("LLM App with Memory 🧠")
+st.caption("LLM App with personalized memory layer that remembers ever user's choice and interests")
+
+openai_api_key = st.text_input("Enter OpenAI API Key", type="password")
+
+if openai_api_key:
+    # Initialize OpenAI client
+    client = OpenAI(api_key=openai_api_key)
+
+    # Initialize Mem0 with Qdrant
+    config = {
+        "vector_store": {
+            "provider": "qdrant",
+            "config": {
+                "host": "localhost",
+                "port": 6333,
+            }
+        },
+    }
+
+    memory = Memory.from_config(config)
+
+    user_id = st.text_input("Enter your Username")
+
+    prompt = st.text_input("Ask ChatGPT")
+
+    if st.button('Chat with LLM'):
+        with st.spinner('Searching...'):
+            relevant_memories = memory.search(query=prompt, user_id=user_id)
+            # Prepare context with relevant memories
+            context = "Relevant past information:\n"
+
+            for mem in relevant_memories:
+                context += f"- {mem['text']}\n"
+                
+            # Prepare the full prompt
+            full_prompt = f"{context}\nHuman: {prompt}\nAI:"
+
+            # Get response from GPT-4
+            response = client.chat.completions.create(
+                model="gpt-4o",
+                messages=[
+                    {"role": "system", "content": "You are a helpful assistant with access to past conversations."},
+                    {"role": "user", "content": full_prompt}
+                ]
+            )
+            
+            answer = response.choices[0].message.content
+
+            st.write("Answer: ", answer)
+
+            # Add AI response to memory
+            memory.add(answer, user_id=user_id)
+
+
+    # Sidebar option to show memory
+    st.sidebar.title("Memory Info")
+    if st.sidebar.button("View Memory Info"):
+        memories = memory.get_all(user_id=user_id)
+        if memories:
+            st.sidebar.write(f"You are viewing memory for user **{user_id}**")
+            for mem in memories:
+                st.sidebar.write(f"- {mem['text']}")
+        else:
+            st.sidebar.info("No learning history found for this user ID.")

llm_app_personalized_memory/requirements.txt

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+streamlit 
+openai
+mem0ai